Automated Organization ProfileApplied Tumor Genomics Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
Applied Tumor Genomics Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This record contains the training, test and validation datasets used to train and evaluate the machine learning models in manuscript: Sahu, Biswajyoti, et al. "Sequence determinants of human gene regulatory elements." (2021).
This record contains also the final hyperparameter-optimized models for each training dataset/task combination described in the manuscript. The README-files provided with the record describe the datasets and models in more detail. The datasets deposited here are derived from the original raw data (GEO accession: GSE180158) as described in the Methods of the manuscript.
Authors
- Sahu, Biswajyoti ;
- Hartonen, Tuomo ;
- Pihlajamaa, Päivi ;
- Wei, Bei ;
- Dave, Kashyap ;
- Zhu, Fangjie ;
- Kaasinen, Eevi ;
- Lidschreiber, Katja ;
- Lidschreiber, Michael ;
- Daub, Carsten O ;
- Cramer, Patrick ;
- Kivioja, Teemu ;
- Taipale, Jussi
This record contains the training, test and validation datasets used to train and evaluate the machine learning models in manuscript: Sahu, Biswajyoti, et al. "Sequence determinants of human gene regulatory elements." (2021).
This record contains also the final hyperparameter-optimized models for each training dataset/task combination described in the manuscript. The README-files provided with the record describe the datasets and models in more detail. The datasets deposited here are derived from the original raw data (GEO accession: GSE180158) as described in the Methods of the manuscript.
Authors
- Sahu, Biswajyoti ;
- Hartonen, Tuomo ;
- Pihlajamaa, Päivi ;
- Wei, Bei ;
- Dave, Kashyap ;
- Zhu, Fangjie ;
- Kaasinen, Eevi ;
- Lidschreiber, Katja ;
- Lidschreiber, Michael ;
- Daub, Carsten O ;
- Cramer, Patrick ;
- Kivioja, Teemu ;
- Taipale, Jussi